Dog Breed Classification using DL

Project Code :TCMAPY1002

Objective

The goal of Dog Breed Classification using Deep Learning (DL) is to create an automated system that is reliable and accurate and can recognize and classify various dog breeds from input photographs. The system seeks to learn detailed traits and patterns unique to each breed through the use of DL techniques like Convolutional Neural Networks (CNNs), which will allow it to make accurate predictions. Numerous real-world uses for this classification exist, such as pet adoption, veterinary diagnostics, and research. The algorithm can help to reduce human error, save time, and provide important insights into the genetic and phenotypic variances across different dog breeds by obtaining high classification accuracy.

Abstract

In this paper, we have proposed two models to classify dogs according to their breeds. Since the classification of dogs is becoming very difficult and moreover, these classifications are taken on the deep learning concept and training a fully defined data set helps in training both models which predicts the different accuracy levels at both ends. Since every now and then predictions are taken for every model. During the study, we came across many types of functionality levels that were not taken in previous studies too. Also, our approach also works on the main concept of transfer learning which deals with data augmentation technique with its properties to increase the size of data set, after which accuracy levels are matched or it is compared with both the models so that a comparison can be made for both the models and the classification is also done with a profound approach.

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